Triple
T17238823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Mallon |
E418433
|
entity |
| Predicate | knownCasesLinked |
P126502
|
FINISHED |
| Object | at least 51 typhoid fever cases |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: at least 51 typhoid fever cases | Statement: [Mary Mallon, knownCasesLinked, at least 51 typhoid fever cases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownCasesLinked Context triple: [Mary Mallon, knownCasesLinked, at least 51 typhoid fever cases]
-
A.
relatedCase
Indicates that one legal case is connected or associated with another case, such as through shared facts, parties, issues, or procedural history.
-
B.
relatedCaseArea
Indicates that one case is associated with, pertains to, or falls within the subject matter or domain of another case area.
-
C.
knownHumanCasesOutcome
Indicates the outcome or result experienced by human cases that have been identified or documented.
-
D.
associatedCourtCase
Indicates a relationship where one entity is linked to, or involved in, a particular court case.
-
E.
linkedLaw
Indicates that there is a connection or reference between an entity and a specific law or legal provision.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dfdc8688190aceb223c19a48781 |
completed | April 19, 2026, 1:21 a.m. |
| PD | Predicate disambiguation | batch_69e3832553ac819091aa917c84f755b6 |
completed | April 18, 2026, 1:12 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:39 a.m.